This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Background: This project aims to identify active-site structural polymorphism, or &#65533;soft spots&#65533;, in order to provide new tools for use in the study of ligand recognition and structure-based drug design. In the majority of cancers, the a specific kinase is the putative drug target (e.g. Abl for chronic myeloid leukemia). Because the active sites of most kinases are structurally similar, targeting the active conformation of a particular kinase over others in the cell can be challenging. As can be seen in the example of Gleevec, however, stabilizing unique, inactive protein conformations provides an attactive alternate paradigm. Unfortunately, it is extremely difficult to predict potential ligand-induced structural rearrangements in unbound proteins, as this task requires determining ensembles of structures instead of just the most populated conformation. I propose to address this issue by combining and extending existing electron density sampling methods to generate ensembles of protein structures for X-ray crystallographic data. Objective: We hypothesize that by selectively sampling electron density, we will be able to identify multiple, unique conformations of protein targets. This new tool can then be used to investigate the biophysical properties of ligand binding and specificity. It can also be used to aid in designing new small molecules with increased specificity with in a protein fold or functional family. Specific Aims: (1) Combine and extend tau-values&#65533;a quantitative metric of side-chain disorder&#65533;and the Ringer program&#65533;a method for building alternate side chain rotamers into weak electron density features&#65533;to identify and characterize soft spots in model systems;(2) compare electron density analysis with orthogonal experimental data for calmodulin-peptide complexes to evaluate the effect of soft spot rearrangements on binding specificity;and (3) apply electron density sampling to identify and characterize potential ligand-induced rearrangements as soft spots in the apo structure of protein drug targets. Study Design: To detect active site excursions, we will elaborate two new methods&#65533;tau values and Ringer&#65533;to computationally analyze X-ray electron density. To test the idea that the electron density of free receptors contains structural information about accessible conformations of the bounds state(s), we will compare crystallographic, NMR and calorimetric data for free calmodulin to calmodulin in complex with five distinct peptides. Finally, we will compare several disease-related protein targets in both the apo form and bound to various drug-like molecules. In each case, we will predict which residues of the free receptor can adjust to different ligands, and then evaluate these predictions using the bound conformations. Once the new method has been developed and validated, we will perform a prospective study where we will predict how the protein MPtpA will respond to binding of inhibitor analogs and test the predictions using X-ray cocrystal structures. Cancer Relevance: Because the active sites of putative cancer targets, including kinases, have been shown to rearrange both as part of their natural function and in response to inhibitor binding, these methods have the potential to provide powerful new tools for structure-based drug design and to advance biophysical understanding of ligand binding. With this new electron density analysis technique, we will be able to predict these rearrangements from a single crystal structure and apply this knowledge both to develop therapeutics with improved specificity as well as provide insight into the mechanism of structural rearrangement necessary for biological activity.